Kostas Karpouzis

Kostas Karpouzis, Dimitris Pantazatos, Joanna Taouki et al.

The advent of Generative AI (GenAI) in education presents a transformative approach to traditional teaching methodologies, which often overlook the diverse needs of individual students. This study introduces a GenAI tool, based on advanced natural language processing, designed as a digital assistant for educators, enabling the creation of customized lesson plans. The tool utilizes an innovative feature termed 'interactive mega-prompt,' a comprehensive query system that allows educators to input detailed classroom specifics such as student demographics, learning objectives, and preferred teaching styles. This input is then processed by the GenAI to generate tailored lesson plans. To evaluate the tool's effectiveness, a comprehensive methodology incorporating both quantitative (i.e., % of time savings) and qualitative (i.e., user satisfaction) criteria was implemented, spanning various subjects and educational levels, with continuous feedback collected from educators through a structured evaluation form. Preliminary results show that educators find the GenAI-generated lesson plans effective, significantly reducing lesson planning time and enhancing the learning experience by accommodating diverse student needs. This AI-driven approach signifies a paradigm shift in education, suggesting its potential applicability in broader educational contexts, including special education needs (SEN), where individualized attention and specific learning aids are paramount

Styliani Zygotegou, Georgios Anastassakis, Georgios Tsatiris et al.

We explore novel game mechanics and techniques in the domain of gamified and game-based mobile mental health applications. By combining modern game design elements with techniques applied by practitioners (e.g., therapists) and known mechanics used in relevant games, we developed an integrated mobile game. Playtesting with a group of individuals showed a positive response towards the study's claims and a promising direction for further research.

Konstantina Ntretska, Nikos Avrantinis, George Tsatiris et al.

Research has shown that digital game players often feel engagement and rapport with a game hero or character when they can channel their own ambitions and goals through the hero's journey in the game world; in essence, they feel a sense of accomplishment and fulfilment whenever they put the game mechanics to use to help the hero reach a positive ending to the game quests. In the case of cooperative gameplay, rapport also has to do with their perception of their peers' skills, gameplay style and behaviour within the game. In this paper, we describe an experiment to identify whether matching players with different personalities, as characterized by the OCEAN or Big-5 personality model, can influence their player experience with a custom-made, cooperative game.

Markos Naftis, George Tsatiris, Kostas Karpouzis

In video games, players' perception of the game world and related information depends on their or the game designer's choice of a virtual camera model. In this paper, we attempt to answer the research question of whether it is possible to identify which camera model is preferred by, fits and best serves each player depending on where they are in a game world and the kinds of challenges they face. To this end, a special type of video game, combining challenges from different game genres, was designed and developed with Unity; thirty players could choose from four camera models at their disposal, depending on where they were in the game world, and utilize the most suitable one to proceed. Each player's preference of camera model was collected using the data platform Unity Analytics and then analyzed. The analysis of the results showed that players managed to adapt to the logic and requirements of the game challenges by choosing different cameras for each of them, depending on the spatial requirements and the presence of enemies or platforms they should jump across from.

Kostas Karpouzis, George Tsatiris

This chapter outlines the relation between artificial intelligence (AI) / machine learning (ML) algorithms and digital games. This relation is two-fold: on one hand, AI/ML researchers can generate large, in-the-wild datasets of human affective activity, player behaviour (i.e. actions within the game world), commercial behaviour, interaction with graphical user interface elements or messaging with other players, while games can utilise intelligent algorithms to automate testing of game levels, generate content, develop intelligent and responsive non-player characters (NPCs) or predict and respond player behaviour across a wide variety of player cultures. In this work, we discuss some of the most common and widely accepted uses of AI/ML in games and how intelligent systems can benefit from those, elaborating on estimating player experience based on expressivity and performance, and on generating proper and interesting content for a language learning game.

George Tsatiris, Kostas Karpouzis

This paper describes the development needed to support the functional and teaching requirements of iRead, a 4-year EU-funded project which produced an award-winning serious game utilising lexical and syntactical game content. The main functional requirement was that the game should retain different profiles for each student, encapsulating both the respective language model (which language features should be taught/used in the game first, before moving on to more advanced ones) and the user model (mastery level for each feature, as reported by the student's performance in the game). In addition to this, researchers and stakeholders stated additional requirements related to learning objectives and strategies to make the game more interesting and successful; these were implemented as a set of selection rules which take into account not only the mastery level for each feature, but also respect the priorities set by teachers, helping avoid repetition of content and features, and maintaining a balance between new content and revision of already mastered features to give students the sense of progress, while also reinforcing learning.

Dimitris Kritikos, Kostas Karpouzis

Synaesthesia is a condition that enables people to sense information in the form of several senses at once. This work describes a Python implementation of a simulation of synaesthesia between listening to music and viewing a painting. Based on Scriabin's definition, we developed a deterministic process to produce a melody after processing a painting, mimicking the production of notes from colours in the field of view of persons experiencing synaesthesia.

Georgios Tsatiris, Kostas Karpouzis, Stefanos Kollias

Human activity recognition and analysis has always been one of the most active areas of pattern recognition and machine intelligence, with applications in various fields, including but not limited to exertion games, surveillance, sports analytics and healthcare. Especially in Human-Robot Interaction, human activity understanding plays a crucial role as household robotic assistants are a trend of the near future. However, state-of-the-art infrastructures that can support complex machine intelligence tasks are not always available, and may not be for the average consumer, as robotic hardware is expensive. In this paper we propose a novel action sequence encoding scheme which efficiently transforms spatio-temporal action sequences into compact representations, using Mahalanobis distance-based shape features and the Radon transform. This representation can be used as input for a lightweight convolutional neural network. Experiments show that the proposed pipeline, when based on state-of-the-art human pose estimation techniques, can provide a robust end-to-end online action recognition scheme, deployable on hardware lacking extreme computing capabilities.